To improve the scalability of meta-level systems, Popper [1]}, [2]} does not precompute every possible rule in the hypothesis space.
Instead, Popper lazily generates rules.
The key idea of Popper is to discover constraints from smaller rules (and hypotheses) to rule out larger rules.
Popper can learn optimal and recursive programs and perform predicate invention.
However, Popper searches for a single solution for all the examples and struggles to learn solutions with many literals.
As we experimentally demonstrate, our DCC approach can substantially outperform Popper and other systems.
